{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "from sklearn.datasets import make_classification\n",
    "from sklearn.metrics import brier_score_loss,log_loss\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.calibration import CalibratedClassifierCV,calibration_curve\n",
    "from sklearn import metrics\n",
    "from sklearn.ensemble import GradientBoostingClassifier\n",
    "\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "np.random.seed(42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "X, y = make_classification(n_samples=150000,n_features=10,n_informative=5,n_redundant=5, class_sep=0.05)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.60)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GradientBoostingClassifier(criterion='friedman_mse', init=None,\n",
       "              learning_rate=0.1, loss='deviance', max_depth=3,\n",
       "              max_features=None, max_leaf_nodes=None,\n",
       "              min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "              min_samples_leaf=1, min_samples_split=2,\n",
       "              min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "              presort='auto', random_state=None, subsample=1.0, verbose=0,\n",
       "              warm_start=False)"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf = GradientBoostingClassifier()\n",
    "clf.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CalibratedClassifierCV(base_estimator=GradientBoostingClassifier(criterion='friedman_mse', init=None,\n",
       "              learning_rate=0.1, loss='deviance', max_depth=3,\n",
       "              max_features=None, max_leaf_nodes=None,\n",
       "              min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "              min_samples_leaf=1, min_samples_split=2,\n",
       "              min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "              presort='auto', random_state=None, subsample=1.0, verbose=0,\n",
       "              warm_start=False),\n",
       "            cv='prefit', method='sigmoid')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ccv_sig = CalibratedClassifierCV(clf,cv='prefit',method='sigmoid')\n",
    "ccv_sig.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "CalibratedClassifierCV(base_estimator=GradientBoostingClassifier(criterion='friedman_mse', init=None,\n",
       "              learning_rate=0.1, loss='deviance', max_depth=3,\n",
       "              max_features=None, max_leaf_nodes=None,\n",
       "              min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "              min_samples_leaf=1, min_samples_split=2,\n",
       "              min_weight_fraction_leaf=0.0, n_estimators=100,\n",
       "              presort='auto', random_state=None, subsample=1.0, verbose=0,\n",
       "              warm_start=False),\n",
       "            cv='prefit', method='isotonic')"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ccv_iso = CalibratedClassifierCV(clf,cv='prefit',method='isotonic')\n",
    "ccv_iso.fit(X_train,y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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t0uyJGsnIK+X5uZv5buMB7AGKMf1acvO5rWgZH252NFFNCxYsYNy4cYSFhTFq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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f63a29ad908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,axes = plt.subplots(1,3,sharey=True)\n",
    "\n",
    "y_preds = clf.predict_proba(X_test)\n",
    "\n",
    "clf_preds = y_preds[:,1]\n",
    "\n",
    "fpr, tpr, _ = metrics.roc_curve(y_test, clf_preds)\n",
    "\n",
    "auc_score = metrics.auc(fpr, tpr)\n",
    "\n",
    "axes[0].set_title('ROC Curve - original classifier')\n",
    "axes[0].plot(fpr, tpr, label='AUC = {:.5f}'.format(auc_score))\n",
    "\n",
    "axes[0].plot([0,1],[0,1],'k:')\n",
    "\n",
    "axes[0].set_xlim([-0.1,1.1])\n",
    "axes[0].set_ylim([-0.1,1.1])\n",
    "axes[0].set_ylabel('True Positive Rate')\n",
    "axes[0].set_xlabel('False Positive Rate')\n",
    "\n",
    "axes[0].legend(loc='lower right')\n",
    "\n",
    "\n",
    "## CCV sigmoid\n",
    "\n",
    "y_preds = ccv_sig.predict_proba(X_test)\n",
    "\n",
    "ccv_preds_sig = y_preds[:,1]\n",
    "\n",
    "fpr, tpr, _ = metrics.roc_curve(y_test, ccv_preds_sig)\n",
    "\n",
    "auc_score = metrics.auc(fpr, tpr)\n",
    "\n",
    "axes[1].set_title('ROC Curve - Calibrated classifier')\n",
    "axes[1].plot(fpr, tpr, label='AUC = {:.5f}'.format(auc_score))\n",
    "\n",
    "axes[1].plot([0,1],[0,1],'k:')\n",
    "\n",
    "axes[1].set_xlim([-0.1,1.1])\n",
    "axes[1].set_ylim([-0.1,1.1])\n",
    "axes[1].set_ylabel('True Positive Rate')\n",
    "axes[1].set_xlabel('False Positive Rate')\n",
    "\n",
    "axes[1].legend(loc='lower right')\n",
    "\n",
    "\n",
    "## CCV isotonic\n",
    "\n",
    "y_preds = ccv_iso.predict_proba(X_test)\n",
    "\n",
    "ccv_preds_iso = y_preds[:,1]\n",
    "\n",
    "fpr, tpr, _ = metrics.roc_curve(y_test, ccv_preds_iso)\n",
    "\n",
    "auc_score = metrics.auc(fpr, tpr)\n",
    "\n",
    "axes[2].set_title('ROC Curve - Calibrated classifier')\n",
    "axes[2].plot(fpr, tpr, label='AUC = {:.5f}'.format(auc_score))\n",
    "\n",
    "axes[2].plot([0,1],[0,1],'k:')\n",
    "\n",
    "axes[2].set_xlim([-0.1,1.1])\n",
    "axes[2].set_ylim([-0.1,1.1])\n",
    "axes[2].set_ylabel('True Positive Rate')\n",
    "axes[2].set_xlabel('False Positive Rate')\n",
    "\n",
    "axes[2].legend(loc='lower right')\n",
    "\n",
    "\n",
    "plt.gcf().set_size_inches(15,5)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f63a2840208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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YMbjttpPs2xfNjBkRVK4c6IgynlqMRS4F//wD994Lmza5UczNmwckjKVLoV07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\nAP4fnT8xqP8EFLMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f63a28403c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.clf()\n",
    "\n",
    "fig, axes = plt.subplots(1,2,sharey=True)\n",
    "\n",
    "# SIGMOID CALIBRATION\n",
    "\n",
    "ax=axes[0]\n",
    "\n",
    "ax.set_xlim([-0.1,1.1])\n",
    "ax.set_ylim([-0.1,1.1])\n",
    "\n",
    "ax.plot([0, 1], [0, 1], \"k:\", label=\"Perfect calibration\")\n",
    "\n",
    "clf_score = brier_score_loss(y_test, clf_preds, pos_label=1)\n",
    "fraction_of_positives, mean_predicted_value = calibration_curve(y_test, clf_preds, n_bins=30)\n",
    "ax.plot(mean_predicted_value, fraction_of_positives, \"r-\", label=\"No Calibration (Brier loss={:.3f})\".format(clf_score))\n",
    "\n",
    "clf_score = brier_score_loss(y_test, ccv_preds_sig, pos_label=1)\n",
    "fraction_of_positives, mean_predicted_value = calibration_curve(y_test, ccv_preds_sig, n_bins=30)\n",
    "ax.plot(mean_predicted_value, fraction_of_positives, \"b-\", label=\"Sigmoid Calibration (Brier loss={:.3f})\".format(clf_score))\n",
    "\n",
    "ax.legend(loc='lower right')\n",
    "ax.set_title('Original vs Sigmoid Calibration', size=16)\n",
    "plt.subplots_adjust(top=0.85)\n",
    "\n",
    "# ISOTONIC CALIBRATION\n",
    "\n",
    "ax=axes[1]\n",
    "\n",
    "ax.set_xlim([-0.1,1.1])\n",
    "ax.set_ylim([-0.1,1.1])\n",
    "\n",
    "ax.plot([0, 1], [0, 1], \"k:\", label=\"Perfect calibration\")\n",
    "\n",
    "clf_score = brier_score_loss(y_test, clf_preds, pos_label=1)\n",
    "fraction_of_positives, mean_predicted_value = calibration_curve(y_test, clf_preds, n_bins=30)\n",
    "ax.plot(mean_predicted_value, fraction_of_positives, \"r-\", label=\"No Calibration (Brier loss={:.3f})\".format(clf_score))\n",
    "\n",
    "clf_score = brier_score_loss(y_test, ccv_preds_iso, pos_label=1)\n",
    "fraction_of_positives, mean_predicted_value = calibration_curve(y_test, ccv_preds_iso, n_bins=30)\n",
    "ax.plot(mean_predicted_value, fraction_of_positives, \"b-\", label=\"Isotonic Calibration (Brier loss={:.3f})\".format(clf_score))\n",
    "\n",
    "ax.legend(loc='lower right')\n",
    "ax.set_title('Original vs Isotonic Calibration', size=16)\n",
    "plt.subplots_adjust(top=0.85)\n",
    "\n",
    "plt.gcf().set_size_inches(12,6)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
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